Update main.py

main.py

@@ -1,61 +1,47 @@
-# main.py
-
-from fastapi import FastAPI
-from pydantic import BaseModel
-from transformers import BartForConditionalGeneration, BartTokenizer, MBartForConditionalGeneration, MBartTokenizer
-import torch
-
-# FastAPI қосымшасы
-app = FastAPI(title="Multilingual Text Summarizer")
-
-# ==========================
-# Модельдерді жүктеу
-# ==========================
-
-# English BART
-MODEL_NAME_EN = "facebook/bart-large-cnn"
-tokenizer_en = BartTokenizer.from_pretrained(MODEL_NAME_EN)
-model_en = BartForConditionalGeneration.from_pretrained(MODEL_NAME_EN)
-
-# Russian MBart
-MODEL_NAME_RU = "IlyaGusev/mbart_ru_sum_gazeta"
-tokenizer_ru = MBartTokenizer.from_pretrained(MODEL_NAME_RU)
-model_ru = MBartForConditionalGeneration.from_pretrained(MODEL_NAME_RU)
-
-# ==========================
-# API Request схемасы
-# ==========================
-class TextRequest(BaseModel):
-    text: str
-    lang: str  # "en" немесе "ru"
-
-# ==========================
-# Routes
-# ==========================
+from fastapi import FastAPI, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+
+from summarizer import generate_summary
+from pdf_reader import extract_text_from_pdf
+
+app = FastAPI()
+
+# CORS (frontend ↔ backend)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Барлық файлдар бір папкада → static ретінде қосамыз
+app.mount("/static", StaticFiles(directory="."), name="static")
+
+# Frontend main page
 @app.get("/")
-def root():
-    return {"message": "Multilingual Text Summarizer is running!"}
-
-@app.post("/summarize/")
-def summarize(request: TextRequest):
-    # Тілді таңдау
-    if request.lang.lower() == "ru":
-        tokenizer = tokenizer_ru
-        model = model_ru
-    else:
-        tokenizer = tokenizer_en
-        model = model_en
-
-    # Tokenize
-    inputs = tokenizer([request.text], max_length=1024, return_tensors="pt", truncation=True)
-
-    # Summarize
-    summary_ids = model.generate(
-        inputs["input_ids"],
-        num_beams=4,
-        max_length=150,
-        early_stopping=True
-    )
-
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+async def root():
+    return FileResponse("index.html")
+
+# TEXT summarization
+@app.post("/summarize/text")
+async def summarize_text(data: dict):
+    text = data.get("text", "")
+    if not text.strip():
+        return {"summary": "No text provided."}
+
+    summary = generate_summary(text)
+    return {"summary": summary}
+
+# PDF summarization
+@app.post("/summarize/pdf")
+async def summarize_pdf(file: UploadFile = File(...)):
+    pdf_bytes = await file.read()
+    text = extract_text_from_pdf(pdf_bytes)
+
+    if not text.strip():
+        return {"summary": "PDF is empty or could not be processed."}
+
+    summary = generate_summary(text)
     return {"summary": summary}